Publication Type

Journal Article

Version

submittedVersion

Publication Date

4-2024

Abstract

We study the wild bootstrap inference for instrumental variable regressions under an alternative asymptotic framework that the number of independent clusters is fixed, the size of each cluster diverges to infinity, and the within cluster dependence is sufficiently weak. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. Second, we establish the conditions for the bootstrap tests to have power against local alternatives. We further develop a wild bootstrap Anderson–Rubin test for the full-vector inference and show that it controls size asymptotically even under weak identification in all clusters. We illustrate their good performance using simulations and provide an empirical application to a well-known dataset about US local labor markets.

Keywords

Clustered data, Randomization test, Weak instrument, Wild bootstrap

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

241

Issue

1

First Page

1

Last Page

21

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2024.105727

Publisher

Elsevier: 24 months

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1016/j.jeconom.2024.105727

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